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Record W6901506116 · doi:10.60692/g3bqs-9hp18

Structural Analysis of Nano Core PCF With Fused Cladding for Supercontinuum Generation in 6G Networks

2023· article· en· W6901506116 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsSupercontinuumCladding (metalworking)Photonic-crystal fiberDoppler broadeningCore (optical fiber)PhotonicsOptical fiber

Abstract

fetched live from OpenAlex

Abstract The Sixth Generation (6G) networks have identified the use of frequency range between 95 GHz and 3 THz with a targeted data rate of 1 Terabytes/second at the access network for holographic video applications. As is demands broadening of spectrum at the core network, this paper proposes a Supercontinuum Generation (SCG) through photonic crystal fiber (PCF) as it provides excellent broadening of the optical spectrum. Discussed in the paper is supercontinuum generation at high pumping power as per the standards specified by the International Telecommunications Union. The proposed PCF is designed with silicon nanocrystal core and the cladding microstructures is arranged in a fusion approach to effectively optimize the optical parameters such as dispersion, nonlinearity, birefringence, group‐velocity dispersion, and confinement loss. The fused cladding comprises of a flower‐cladding assembly in which air‐holes arrangement is inspired from petals in a pleated structure. Such arrangement is shown here to provide high nonlinearity and negative dispersion for high power supercontinuum generation. The novel nanocore assembly with improved structural constraints delivers a non‐linearity of 6.37 × 10 6 W −1 km −1 and a negative dispersion of −142.1 (ps/nm‐km) at 1,550 nm. Moreover, a supercontinuum spectrum is generated using different pulse widths ranging from 350 to 650 ps with 25 kW pump power for PCF lengths of 10 and 15 mm.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.201
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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